UNICEF REPORT

When Hope Fades:
The Story of Najir

Author: Anthea Petrillo
Published: April 26, 2025

Report Objective

This report aims to raise awareness about the tragic reality of child deaths caused by diarrhoea in children under the age of five. Although preventable and treatable condition, it remains one of the leading causes of child mortality in many low-income countries, where access to healthcare is limited.

The narrative voice of this report is that of Najir, a 4-year-old child who tells his story: from when he tells his mother he has a stomach ache, to when, despite efforts to find a cure, he sadly does not survive. Although this is a fictional story, it is plausible enough to reflect what happens in countries with low access to diarrhoea care, where the inadequacy of healthcare resources is tied to economic and social inequalities.

Data Sources

The data used in this report comes from the following datasets:

  1. unicef_indicator_1.csv: provides global data on the percentage of children under five with diarrhoea who can access healthcare facilities
  2. unicef_indicator_2.csv: offers global data on the percentage of healthcare facilities with basic water access
  3. unicef_metadata.csv: includes economic and social data
  4. deaths_caused_by_diarrhoea.csv: provides data on diarrhoea-related child mortality categorized by seven regions: North America, South Asia, Middle East and North Africa, Europe and Central Asia, Latin America and Caribbean, East Asia and Pacific, Sub-Saharan Africa. You can download it here.

Please find the Tableau version of this report here.

Najir’s Story

My name is Najir, and I am four years old.

I do not feel well. A sharp pain grips my stomach, and my mother tells me I have diarrhoea. She holds my hand tightly, her face etched with worry, her eyes filled with silent fear. “We must find help,” she says. However, seeking medical care is not a simple task.

I live in one of the nineteen countries where, according to data collected between 2017 and 2023, among all the children under the age of five suffering from diarrhoea, fewer than 40% have access to medical advice or treatment at a healthcare facility or from a trained provider.

Healthcare access for children with diarrhoea in lowest-access countries

Code 
import pandas as pd
from plotnine import ggplot, aes, geom_bar, labs, theme_minimal, theme, element_text, scale_fill_gradient, element_rect

df = pd.read_excel("unicef_indicator_1_filtered_firstgraph.xlsx")

df['access to care for diarrhoea'] = df['access to care for diarrhoea'] * 100

bar_chart = (
    ggplot(df, aes(x='country', y='access to care for diarrhoea', fill='access to care for diarrhoea')) 
    + geom_bar(stat='identity', show_legend=False, width=0.35)  
    + scale_fill_gradient(low='#D5006D', high='#FF80AB') 
    + labs(
        x='Country', 
        y='Access to Care for Diarrhoea (%)'
    ) 
    + theme_minimal() 
    + theme(
        figure_size=(8, 5), 
        plot_background=element_rect(fill='#F5FBFF', color='#F5FBFF'),  
        panel_background=element_rect(fill='#F5FBFF', color='#F5FBFF'), 
        panel_grid_major=None, 
        panel_grid_minor=None,
        axis_text_x=element_text(rotation=45, ha='right', size=10, color='#1F407A'),  
        axis_text_y=element_text(size=8, color='#1F407A', weight='bold'),  
        axis_title_x=element_text(size=12, color='#1F407A', weight='bold'),  
        axis_title_y=element_text(size=10, color='#1F407A', weight='bold'),  
        plot_title=element_text(size=0)  
    )
)

bar_chart

A serious public health crisis is evident: limited accessibility and availability of essential healthcare services in specific areas of the world.

Despite being a preventable and treatable condition, diarrhoea remains one of the leading causes of child mortality in countries where healthcare systems are fragile.

According to the 2021 data, diarrhoea accounted for 9% of all deaths in children under 5 - over 1,200 daily, or 440,000 annually.

The following histogram displays the global distribution of child diarrheal mortality in 2021. Each vertical bar represents a specific mortality rate (from 0% to 16%), while its height indicates how many countries fall into that percentage range.

The structure simultaneously reveals three key insights:

  • Extent of the phenomenon (horizontal position on the scale)
  • Geographical distribution (color-coded bar segments)
  • Regional representation (width of colored segments).

The graph clearly demonstrates how diarrheal impact varies dramatically across regions, with a gradient ranging from minimal percentages in developed areas to alarming rates in critical zones.

Mortality is highest in Sub-Saharan Africa.

Healthcare access for children with diarrhoea (top 10 poorest countries)

Code 
import pandas as pd

df = pd.read_csv('unicef_metadata_7.csv', usecols=['country', 'year', 'GDP per capita (constant 2015 US$)'])
df = df[(df['year'] == 2022)].dropna()

top10_high = df.nlargest(10, 'GDP per capita (constant 2015 US$)')['country'].tolist()
top10_low = df.nsmallest(10, 'GDP per capita (constant 2015 US$)')['country'].tolist()

print("Top 10 Highest GDP per Capita in 2022:", top10_high)
print("Top 10 Lowest GDP per Capita in 2022:", top10_low)
Top 10 Highest GDP per Capita in 2022: ['Monaco', 'Bermuda', 'Luxembourg', 'Ireland', 'Switzerland', 'Cayman Islands', 'Norway', 'Singapore', 'United States', 'Qatar']
Top 10 Lowest GDP per Capita in 2022: ['Burundi', 'Afghanistan', 'Central African Republic', 'Madagascar', 'Somalia', 'Congo, the Democratic Republic of the', 'Malawi', 'Niger', 'Chad', 'Mozambique']
Code 
# Libraries
import pandas as pd
import plotly.express as px

# Load the dataset
df = pd.read_csv("unicef_indicator_1 (2).csv")

# List of target countries
countries_of_interest = [
    'Burundi', 'Afghanistan', 'Central African Republic', 'Madagascar', 
    'Somalia', 'Congo, the Democratic Republic of the', 'Malawi', 
    'Niger', 'Chad', 'Mozambique'
]

# Filter dataset: only Male/Female and target countries
df_filtered = df[
    (df["sex"].isin(["Female", "Male"])) &
    (df["country"].isin(countries_of_interest))
]

# Calculate yearly averages by sex
df_grouped = df_filtered.groupby(["time_period", "sex"], as_index=False)["obs_value"].mean()

# Create line plot
fig = px.line(
    df_grouped,
    x="time_period",
    y="obs_value",
    color="sex",
    labels={
        "time_period": "<b>Year</b>",
        "obs_value": "<b>Average Value (%)</b>",
        "sex": "<b>Sex</b>"
    },
    color_discrete_map={
        "Female": "#FF69B4",  # Pink
        "Male": "#00BFFF"     # Blue
    }
)

# Style improvements
fig.update_traces(mode="lines+markers", line_width=2.5)
fig.update_layout(
    plot_bgcolor='rgba(245, 245, 245, 0.1)',  # Very light sandstone background
    paper_bgcolor='rgba(245, 245, 245, 0.1)',
    xaxis=dict(
        tickmode="linear",
        title_font=dict(color="#1F407A", size=14),  # Dark blue bold
        tickfont=dict(color="#333333")
    ),
    yaxis=dict(
        range=[0, 100],
        title_font=dict(color="#1F407A", size=14),  # Dark blue bold
        tickfont=dict(color="#333333")
    ),
    legend_title_font=dict(color="#1F407A"),  # Dark blue legend title
    legend_font=dict(color="#333333"),        # Dark text for legend items
    hoverlabel=dict(
        bgcolor="white",
        font_size=12,
        font_family="Arial"
    )
)

fig.show()

There is a hospital in the next village, but the water from the tap tastes like dirt. My mother says that in richer countries, the water runs clear and doctors always have sterile gloves. I’ve never seen clean water myself, maybe if I had, my stomach wouldn’t hurt like this.

When we finally reached the hospital, the doctors just shook their heads. ‘We don’t have basic water services here,’ they told us. My mother held me tighter, but there was nothing they could do.

In fact, the percentage of healthcare facilities with access to basic water services is not the same across the world.

The lack of access to basic water services means that healthcare facilities are unable to treat children suffering from diarrhoea.

Health care facilities & water access: country comparison 2021

Code 
import pandas as pd
import plotly.express as px
import warnings

warnings.filterwarnings('ignore')

df = pd.read_csv("unicef_indicator_2 (2).csv")

df_2021 = df[
    (df["time_period"] == 2021) &
    (df["unit_of_measure"] == "%")
].copy()  

df_2021["% Water Access"] = df_2021["obs_value"].apply(lambda x: f"{x:.1f}%")

fig = px.choropleth(
    df_2021,
    locations="alpha_3_code",
    color="obs_value",
    hover_name="country",
    hover_data={
        "alpha_3_code": False,
        "obs_value": False,
        "% Water Access": True
    },
    color_continuous_scale="Blues"
)

fig.update_layout(
    coloraxis_colorbar=dict(
        title="<b>Water Access (%)</b>",
        tickformat=".1f",
        title_font=dict(color="#1F407A", size=12),
        tickfont=dict(color="#1F407A")
    ),
    title=None
)

fig.show()

In countries like mine, parents often have large families because they sadly anticipate that some of their children may not survive. This leads to very high birth rates. Since birth rates and life expectancy are inversely related, higher birth rates typically correspond to lower life expectancy at birth.

My mother says that in rich countries, parents have few children and live long enough to become grandparents. Here, instead, who knows if I will even grow up to be as old as my father.

Demographic patterns (2021): Birth rate & Life expectancy

Code 
import pandas as pd
import plotly.express as px

df = pd.read_csv("unicef_metadata_7.csv")

df_2021 = df[df["year"] == 2021]

df_2021 = df_2021.dropna(subset=["Birth rate, crude (per 1,000 people)", "Life expectancy at birth, total (years)"])

fig = px.scatter(
    df_2021,
    x="Birth rate, crude (per 1,000 people)",
    y="Life expectancy at birth, total (years)",
    hover_name="country",
    trendline="ols",
    color_discrete_sequence=["#FF80AB"],
    trendline_color_override="#4B0082",
    labels={
        "Birth rate, crude (per 1,000 people)": "Birth Rate (per 1,000 people)",
        "Life expectancy at birth, total (years)": "Life Expectancy (years)"
    }
)

fig.update_layout(
    plot_bgcolor="#F5FBFF",
    paper_bgcolor="#F5FBFF",
    xaxis_title="<b>Birth Rate (per 1,000 people)</b>",
    yaxis_title="<b>Life Expectancy (years)</b>",
    xaxis=dict(
        title_font=dict(color="#1F407A", size=14), 
        tickfont=dict(color="#333333", size=12)   
    ),
    yaxis=dict(
        title_font=dict(color="#1F407A", size=14), 
        tickfont=dict(color="#333333", size=12) 
    ),
    hoverlabel=dict(
        bgcolor="white",
        font_size=12
    )
)

fig.show()

In the following chart, the disparity in birth rates between the poorest countries (pink trend line) and the richest countries (purple trend line) is even more evident.